Abstract: Advanced spatial transcriptomics (ST) techniques provide comprehensive insights into complex organisms across multiple scales, while simultaneously posing challenges in bioimage analysis. The spatial co-profiling of biological tissues by gigapixel whole slide images (WSI) and gene expression arrays motivates the development of innovative and efficient algorithmic approaches. Using Generative Adversarial Nets (GAN), we introduce **I**nfinite **S**patial **T**ranscriptomic **e**diting (IST-editing) and establish gene expression-guided editing in a generated gigapixel mouse pup. Trained with patch-wise high-plex gene expression (input) and matched image data (output), IST-editing enables the seamless synthesis of arbitrarily large bioimages at inference, *e.g.*, with a $106496 \times 53248$ resolution. After feeding edited gene expressions to the trained model, we simulate cell-, tissue- and animal-level morphological transitions in the generated mouse pup. Lastly, we discuss and evaluate editing effects on interpretable morphological features. The code and generated WSIs are publicly accessible via https://github.com/CTPLab/IST-editing.